III Arsenide

Frank‐Rotsch, Ch.; Dropka, Natasha; Rotsch, Peter

doi:10.1016/b978-0-08-102096-8.00006-9

Cited by 7 publications

(9 citation statements)

References 111 publications

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“…In this study, we chose the setvalues of temperatures in GaAs from the typical values of temperature gradients in the crystal and the melt in industrial processes, which are in the range of 15 K cm −1 and 2-10 K cm −1 , respectively. [21] In addition to the RMSE values, the results were also discussed with regard to the inputs (features) importance determined using the Random Forest (RF) method.…”

Section: Data-driven Methodsmentioning

confidence: 99%

Development of the VGF Crystal Growth Recipe: Intelligent Solutions of Ill‐Posed Inverse Problems using Images and Numerical Data

Dropka,

Holena,

Thieme

et al. 2023

Cryst. Res. Technol.

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Development of the Vertical Growth Freeze crystal growth process is a typical example of solving the ill‐posed inverse problem, which violates one or more of Hadamard's well‐posedness criteria of solution existence, uniqueness, and stability. In this study, different data‐driven approaches are used to solve inverse problems: Reduced Order Modelling method of Proper Orthogonal Decomposition with Inverse Distance weighting (ROM POD InvD), an approximation method of Kriging and Artificial Neural Networks (ANN) employing images, combination of images and numerical data and solely numerical data, respectively. The ≈200 training data are generated by Computational Fluid Dynamics (CFD) simulations of the forward problem. Numerical input data are related to the temperatures and coordinates in 10 characteristic monitoring points in the melt and crystal, while the image input data are related to the interface shape and position. Using the random mean squared error as a criterion, the Kriging method based on images and numerical data and the ANN method based on numerical data are able to capture the system behavior more accurately, in contrast to the ROM POD InvD method, which is based solely on images.

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Section: Data-driven Methodsmentioning

confidence: 99%

Development of the VGF Crystal Growth Recipe: Intelligent Solutions of Ill‐Posed Inverse Problems using Images and Numerical Data

Dropka,

Holena,

Thieme

et al. 2023

Cryst. Res. Technol.

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“…Weak buoyancy-driven melt convection [22] was described by the laminar flow model. All CFD simulations were performed using quasi steady-state (QSS) assumption.…”

Section: ∇•mentioning

confidence: 99%

“…The GaAs material properties used in this study are given in [22]. The crystals were grown in Ar atmosphere under the pressure of 4 bar.…”

Section: ∇•mentioning

confidence: 99%

“…The success of the latter highly depends on the advancement in crystal manufacturing and obtained crystal quality. An additional reason was the axisymmetric nature of the VGF furnace [22] and fulfilled constraints for a quasi steady-state approach [23,24] that assures realistic high volume and high quality of training data. It is important to note that the applied methodology can be easily adopted by other materials and growth processes.…”

Section: Introductionmentioning

confidence: 99%

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Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques

2021

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The aim of this study was to assess the ability of the various data mining and supervised machine learning techniques: correlation analysis, k-means clustering, principal component analysis and decision trees (regression and classification), to derive, optimize and understand the factors influencing VGF-GaAs growth. Training data were generated by Computational Fluid Dynamics (CFD) simulations and consisted of 130 datasets with 6 inputs (growth rate and power of 5 heaters) and 5 outputs (interface position and deflection, and temperatures at various positions in GaAs). Data mining results confirmed a good dispersion of the training data without the feasibility of a dimensionality reduction. Data clustering was observed in relation to the position of the crystallization front relative to the side heaters. Based on the statistical performance criteria and training results, decision trees identified the most decisive inputs and their ranges for a favorable interface shape and to keep GaAs temperature beyond limits for heavy arsenic evaporation. Decision trees are a recommendable machine learning technique with short training times and acceptable predictive accuracy based on small volume of CFD training data, capable of providing guidelines for understanding the crystal growth process, which is a prerequisite for the growth of low-cost, high-quality bulk crystals.

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“…The later originates from low heat conductivity of GaAs (λ m = 0.178 W/cm/K and λ s = 0.071 W/cm/K at T m = 1512 K) that hampers latent heat removal. The growth rate is slow (~2-4 mm/h), process temperature range narrow (grad T melt ~2-5 K/cm; grad T crystal ~15 K/cm) and critical shear stress low (σ cr = 0.587 MPa at 40 K below T m ) [2].…”

Section: Introductionmentioning

confidence: 99%

Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks

2021

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The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs) using datasets generated by numerical transient simulations. Real time predictions of the temperatures and solid–liquid interface position in GaAs are crucial for control applications and for process visualization, i.e., for generation of digital twins. In the reported study, an LSTM network was trained on 1950 datasets with 2 external inputs and 6 outputs. Based on network performance criteria and training results, LSTMs showed the very accurate predictions of the VGF-GaAs growth process with median root-mean-square-error (RMSE) values of 2 × 10−3. This deep learning method achieved a superior predictive accuracy and timeliness compared with more traditional Nonlinear AutoRegressive eXogenous (NARX) recurrent networks.

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III Arsenide

Cited by 7 publications

References 111 publications

Development of the VGF Crystal Growth Recipe: Intelligent Solutions of Ill‐Posed Inverse Problems using Images and Numerical Data

Development of the VGF Crystal Growth Recipe: Intelligent Solutions of Ill‐Posed Inverse Problems using Images and Numerical Data

Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques

Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks

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